The following enumerates some of the objectives of this work. As will
be discuss in the related work section, many other efforts
in the area of behaviour acquisition and synthesis are underway. We
will use this list to specifically distinguish the goals of this
project and motivate its contribution. These are key ingredients that
we will strive and argue for in the development and use of synthetic
behavioural characters.

Behaviour Learning from Autonomous Observation

A system is desired that will learn behaviours autonomously without
any explicit models or instructions. The system will use observations
of natural interactions between humans as training data to acquire
such models in a passive way. The natural interactions will take place
in a constrained scenario to maintain tractability.

Imitation Based Behaviour Learning

The type of learning that will be acquired will be imitation learning
to simulate the kind of behaviour patterns that are observed in
others. The system is to learn how to interact with humans by
imitating prototypical reactions to their actions and learning
mappings of appropriate interactive behaviour.

Fully Perceptual Grounding

The system will use perception and generate perceptual output which
will be the channel through which it performs its acquisition of
behaviour. By sensing external stimuli, it should acquire behaviours
at a perceptual level and ground them in physical and expressive
manifestations (i.e. vision sensing and graphics synthesis).

Unsupervised Automatic Learning

Automatic learning is desired without significant data engineering or
manual specification of models. There should not be external
intervention or direct supervision beyond the specification of inputs
and output features. Rather, the system should learn only from
constrained yet natural human to human interactions without explicit
teaching effort.

No Discrete Alphabet or Set of Actions

There will not be a discrete set of actions and reactions to
facilitate behaviour selection and associative learning. Input will be
in a continuous noisy space where event will seldom be observed
exactly the same way twice. Thus, exact deterministic models or
if-then types of associations which assist the learning process can
not work.

Probability over Reactions Given Actions

A fully probabilistic model of the conditional probability over
possible continuous reactions given continuous actions is
desired. Thus, the system can have some natural stochastic behaviour
as opposed to behaving in a fully predictable manner.

Real-Time, Unencumbered Interaction

The system is to use the acquired behaviours to interact with other
humans in real-time without excessively encumbering the participants. In
other words, the humans will engage the system in a natural way
without extra paraphernalia and without excessively
constraining their behaviour to accommodate the system. In
addition, the system must compute the desired action to take and
manifest it in some output form in an interactive manner.

Behaviour Synthesis and Generalization

The behaviour synthesized has to agree with the acquired behaviours
from observations of human participants. An action on the user's
behave should induce a sensible reaction on the behalf of the
system. The behaviour being synthesized should generalize to
interaction with different users and show some robustness to changes
in operating conditions.

Minimal Structural Specifications

There will be a minimal amount of structure on the system's learning
models. The structures being used for learning should be generic
enough to uncover temporal interaction in other phenomena that are not
only limited to the particular training scenario.

Computational Efficiency

The system must learn quickly with limited time and computational
resources. The system does not have the luxury of exploring a huge
space by trial and error or exhaustive search. In addition, it must
learn from few examples without requiring large redundancies and many
instances of training data.